Everyday, millions browse for media content online or searchable databases by inputting general or very specific terms that articulate or convey a subject's tastes and preferences for media content. Passive and subconscious responses to media experiences that are spontaneous, non-verbal or involuntary can also be reliable indicators of a subject's tastes and preferences. Conscious or subconscious response and reaction behaviors such as blushing, laughing, elevated heart rate, blood pressure changes and the like can be identified and measured with wearable and facial recognition technologies.
Captured behavioral data may provide reference points such that evaluation, estimates and predictions of a subject's taste and preference can be measured and articulated. Ongoing collection of experiential data may offer greater statistical reliability and accuracy in determining a subject's tastes and preferences or their “connectedness” to media content varieties. Such a method could support machine learning systems for media content browsing and advanced search functions that successfully interpret behavioral and biometric data.